# Exact Distributed Training: Random Forest with Billions of Examples

arXiv: https://arxiv.org/abs/1804.06755 · HTML: https://mlsanity.com/paper/1804.06755/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2018.

## Built on by (most cited first, 1 of 1)

- [Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library](https://mlsanity.com/paper/2212.02934/) (2022)

## Abstract (arXiv)

We introduce an exact distributed algorithm to train Random Forest models as well as other decision forest models without relying on approximating best split search. We explain the proposed algorithm and compare it to related approaches for various complexity measures (time, ram, disk, and network complexity analysis). We report its running performances on artificial and real-world datasets of up to 18 billions examples. This figure is several orders of magnitude larger than datasets tackled in the existing literature. Finally, we empirically show that Random Forest benefits from being trained on more data, even in the case of already gigantic datasets. Given a dataset with 17.3B examples with 82 features (3 numerical, other categorical with high arity), our implementation trains a tree in 22h.